National HIV and HCV Screening Rates for Hospitalized People who Use Drugs Are Suboptimal and Heterogeneous Across 11 US Hospitals.

Background: To end the HIV and hepatitis C virus (HCV) epidemics, people who use drugs (PWUD) need more opportunities for testing. While inpatient hospitalizations are an essential opportunity to test people who use drugs (PWUD) for HIV and HCV, there is limited research on rates of inpatient testing for HIV and HCV among PWUD. Methods: Eleven hospital sites were included in the study. Each site created a cohort of inpatient encounters associated with injection drug use. From these cohorts, we collected data on HCV and HIV testing rates and HIV testing consent policies from 65 276 PWUD hospitalizations. Results: Hospitals had average screening rates of 40% for HIV and 32% for HCV, with widespread heterogeneity in screening rates across facilities. State consent laws and opt-out testing policies were not associated with statistically significant differences in HIV screening rates. On average, hospitals that reflexed HCV viral load testing on HCV antibody testing did not have statistically significant differences in HCV viral load testing rates. We found suboptimal testing rates during inpatient encounters for PWUD. As treatment (HIV) and cure (HCV) are necessary to end these epidemics, we need to prioritize understanding and overcoming barriers to testing.

Adherence to stewardship recommendations for antibiotic discontinuation reduces antibiotic-associated adverse drug events.

Inappropriate antibiotic use may lead to increased adverse drug events (ADEs). This study assessed whether an antimicrobial stewardship recommendation to discontinue antibiotics in patients with low likelihood for bacterial infection reduced antibiotic duration and antibiotic-associated ADEs. At a 4-hospital system, hospitalized adult patients receiving empiric antibiotics for suspected infection were identified between May 2, 2016 and June 30, 2018. For those patients who were deemed unlikely to have a bacterial infection, a note was left by an infectious diseases physician recommending antibiotic discontinuation. Patient cases were considered "adherent" to recommendations if antibiotics were discontinued within 48 hours of the note and "non-adherent" to recommendations if antibiotics were continued beyond this. Duration of antibiotics and potential antibiotic-associated ADEs were collected retrospectively. Attribution of the adverse event to the antibiotic was decided upon by the investigators. The incidence of ADEs and duration of antibiotics between the adherent and non-adherent groups were compared. Of 253 patients deemed unlikely to have a bacterial infection, 114 (45%) treatment teams stopped antibiotics within 48 hours of the recommendation, and 139 (55%) continued antibiotics. The total number of ADEs was significantly higher in the non-adherent group compared to the adherent group (34 ADEs vs 9 ADEs, P = .001). The median number of total prescribed antibiotic days was higher in the non-adherent group than in the adherent group (5 days vs 1 day, P < .001). This study demonstrates that stewardship programs may prevent ADEs by recommending antibiotic discontinuation in patients with low suspicion for bacterial infection.

"There hasn't been a push to identify patients in the emergency department"-Staff perspectives on automated identification of candidates for pre-exposure prophylaxis (PrEP): A qualitative study.

Automated algorithms for identifying potential pre-exposure prophylaxis (PrEP) candidates are effective among men, yet often fail to detect cisgender women (hereafter referred to as "women") who would most benefit from PrEP. The emergency department (ED) is an opportune setting for implementing automated identification of PrEP candidates, but there are logistical and practical challenges at the individual, provider, and system level. In this study, we aimed to understand existing processes for identifying PrEP candidates and to explore determinants for incorporating automated identification of PrEP candidates within the ED, with specific considerations for ciswomen, through a focus group and individual interviews with ED staff. From May to July 2021, we conducted semi-structured qualitative interviews with 4 physicians and a focus group with 4 patient advocates working in a high-volume ED in Chicago. Transcripts were coded using Dedoose software and analyzed for common themes. In our exploratory study, we found three major themes: 1) Limited PrEP knowledge among ED staff, particularly regarding its use in women; 2) The ED does not have a standardized process for assessing HIV risk; and 3) Perspectives on and barriers/facilitators to utilizing an automated algorithm for identifying ideal PrEP candidates. Overall, ED staff had minimal understanding of the need for PrEP among women. However, participants recognized the utility of an electronic medical record (EMR)-based automated algorithm to identify PrEP candidates in the ED. Facilitators to an automated algorithm included organizational support/staff buy-in, patient trust, and dedicated support staff for follow-up/referral to PrEP care. Barriers reported by participants included time constraints, hesitancy among providers to prescribe PrEP due to follow-up concerns, and potential biases or oversight resulting from missing or inaccurate information within the EMR. Further research is needed to determine the feasibility and acceptability of an EMR-based predictive HIV risk algorithm within the ED setting.

Assessing Patient Acceptance of an Automated Algorithm to Identify Ciswomen for HIV Pre-Exposure Prophylaxis.

The use of HIV pre-exposure prophylaxis (PrEP) in cisgender women (ciswomen) lags far behind their need. Data elements from the electronic medical record (EMR), including diagnosis of a sexually transmitted infection (STI), can be incorporated into automated algorithms for identifying clients who are most vulnerable to HIV and would benefit from PrEP. However, it is unknown how women feel about the use of such technology. In this study, we assessed women's attitudes and opinions about an automated EMR-based HIV risk algorithm and determined if their perspectives varied by level of HIV risk. Respondents were identified using best practice alerts or referral to a clinic for STI symptoms from January to December 2021 in Chicago, IL. Participants were asked about HIV risk factors, their self-perceived HIV risk, and their thoughts regarding an algorithm to identify ciswomen who could benefit from PrEP. Most of the 112 women who completed the survey (85%) thought they were at low risk for HIV, despite high rates of STI diagnoses. The majority were comfortable with the use of this algorithm, but their comfort level dropped when asked about the algorithm identifying them specifically. Ciswomen had mixed feelings about the use of an automated HIV risk algorithm, citing it as a potentially helpful and empowering tool for women, yet raising concerns about invasion of privacy and potential racial bias. Clinics must balance the benefits of using an EMR-based algorithm for ciswomen with their concerns about privacy and bias to improve PrEP uptake among particularly vulnerable women.

Population-Level Portal-Based Anxiety and Depression Screening Perspectives in HIV Care Clinicians: Qualitative Study Using the Consolidated Framework for Implementation Research.

BACKGROUND: Depression and anxiety are common among people with HIV and are associated with inadequate viral suppression, disease progression, and increased mortality. However, depression and anxiety are underdiagnosed and undertreated in people with HIV owing to inadequate visit time and personnel availability. Conducting population-level depression and anxiety screening via the patient portal is a promising intervention that has not been studied in HIV care settings. OBJECTIVE: We aimed to explore facilitators of and barriers to implementing population-level portal-based depression and anxiety screening for people with HIV. METHODS: We conducted semistructured hour-long qualitative interviews based on the Consolidated Framework for Implementation Research with clinicians at an HIV clinic. RESULTS: A total of 10 clinicians participated in interviews. In total, 10 facilitators and 7 barriers were identified across 5 Consolidated Framework for Implementation Research domains. Facilitators included advantages of systematic screening outside clinic visits; the expectation that assessment frequency could be tailored to patient needs; evidence from the literature and previous experience in other settings; respect for patient privacy; empowering patients and facilitating communication about mental health; compatibility with clinic culture, workflows, and systems; staff beliefs about the importance of mental health screening and benefits for HIV care; engaging all clinic staff and leveraging their strengths; and clear planning and communication with staff. Barriers included difficulty in ensuring prompt response to suicidal ideation; patient access, experience, and comfort using the portal; limited availability of mental health services; variations in how providers use the electronic health record and communicate with patients; limited capacity to address mental health concerns during HIV visits; staff knowledge and self-efficacy regarding the management of mental health conditions; and the impersonal approach to a sensitive topic. CONCLUSIONS: We proposed 13 strategies for implementing population-level portal-based screening for people with HIV. Before implementation, clinics can conduct local assessments of clinicians and clinic staff; engage clinicians and clinic staff with various roles and expertise to support the implementation; highlight advantages, relevance, and evidence for population-level portal-based mental health screening; make screening frequency adaptable based on patient history and symptoms; use user-centered design methods to refine results that are displayed and communicated in the electronic health record; make screening tools available for patients to use on demand in the portal; and create protocols for positive depression and anxiety screeners, including those indicating imminent risk. During implementation, clinics should communicate with clinicians and clinic staff and provide training on protocols; provide technical support and demonstrations for patients on how to use the portal; use multiple screening methods for broad reach; use patient-centered communication in portal messages; provide clinical decision support tools, training, and mentorship to help clinicians manage mental health concerns; and implement integrated behavioral health and increase mental health referral partnerships.

Adapting Provider Training and Pre-Exposure Prophylaxis Advertising to Increase Pre-Exposure Prophylaxis Awareness and Uptake Among Black Cisgender Women.

Black cisgender women (hereafter referred to as "women") have disproportionately high rates of HIV infection yet low rates of pre-exposure prophylaxis (PrEP) utilization. Barriers to PrEP uptake exist at the system, provider, and individual/client level. To learn how existing training and advertising can be adapted to address race- and sex-based gaps within PrEP service delivery, we conducted focus groups with providers and Black women. Participants were recruited at three health care organizations in the Midwest and South, screened for eligibility, and consented verbally. Focus groups occurred from August 2022 to February 2023. Women were asked about their knowledge and thoughts on PrEP. Providers were asked about factors influencing their decision-making about PrEP. A codebook was developed based on the Consolidated Framework for Implementation Research. Transcripts were coded using the Stanford Lightning Report Method. We completed four focus groups with 10 providers and 9 focus groups with 25 women. Three major themes emerged: (1) low comfort level and limited cultural sensitivity/competency among providers discussing HIV risk and PrEP with Black women, (2) women's concerns about PrEP's side effects and safety during pregnancy, and (3) lack of Black women representation in PrEP advertisement/educational materials. In addition, women in the South reported general medical mistrust and specific misconceptions about PrEP. PrEP trainings for providers need detailed information about the safety of PrEP for women and should include role-playing to enhance cultural competency. Likewise, PrEP advertisements/materials should incorporate information regarding side effects and images/experiences of Black women to increase PrEP awareness and uptake among this population. Clinical Trial Registration Number: NCT05626452.

Development of a predictive model for identifying women vulnerable to HIV in Chicago.

INTRODUCTION: Researchers in the United States have created several models to predict persons most at risk for HIV. Many of these predictive models use data from all persons newly diagnosed with HIV, the majority of whom are men, and specifically men who have sex with men (MSM). Consequently, risk factors identified by these models are biased toward features that apply only to men or capture sexual behaviours of MSM. We sought to create a predictive model for women using cohort data from two major hospitals in Chicago with large opt-out HIV screening programs. METHODS: We matched 48 newly diagnosed women to 192 HIV-negative women based on number of previous encounters at University of Chicago or Rush University hospitals. We examined data for each woman for the two years prior to either their HIV diagnosis or their last encounter. We assessed risk factors including demographic characteristics and clinical diagnoses taken from patient electronic medical records (EMR) using odds ratios and 95% confidence intervals. We created a multivariable logistic regression model and measured predictive power with the area under the curve (AUC). In the multivariable model, age group, race, and ethnicity were included a priori due to increased risk for HIV among specific demographic groups. RESULTS: The following clinical diagnoses were significant at the bivariate level and were included in the model: pregnancy (OR 1.96 (1.00, 3.84)), hepatitis C (OR 5.73 (1.24, 26.51)), substance use (OR 3.12 (1.12, 8.65)) and sexually transmitted infections (STIs) chlamydia, gonorrhoea, or syphilis. We also a priori included demographic factors that are associated with HIV. Our final model had an AUC of 0.74 and included healthcare site, age group, race, ethnicity, pregnancy, hepatitis C, substance use, and STI diagnosis. CONCLUSIONS: Our predictive model showed acceptable discrimination between those who were and were not newly diagnosed with HIV. We identified risk factors such as recent pregnancy, recent hepatitis C diagnosis, and substance use in addition to the traditionally used recent STI diagnosis that can be incorporated by health systems to detect women who are vulnerable to HIV and would benefit from preexposure prophylaxis (PrEP).

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation.

BACKGROUND: Reducing care lapses for people living with HIV is critical to ending the HIV epidemic and beneficial for their health. Predictive modeling can identify clinical factors associated with HIV care lapses. Previous studies have identified these factors within a single clinic or using a national network of clinics, but public health strategies to improve retention in care in the United States often occur within a regional jurisdiction (eg, a city or county). OBJECTIVE: We sought to build predictive models of HIV care lapses using a large, multisite, noncurated database of electronic health records (EHRs) in Chicago, Illinois. METHODS: We used 2011-2019 data from the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a database including multiple health systems, covering the majority of 23,580 people with an HIV diagnosis living in Chicago. CAPriCORN uses a hash-based data deduplication method to follow people across multiple Chicago health care systems with different EHRs, providing a unique citywide view of retention in HIV care. From the database, we used diagnosis codes, medications, laboratory tests, demographics, and encounter information to build predictive models. Our primary outcome was lapses in HIV care, defined as having more than 12 months between subsequent HIV care encounters. We built logistic regression, random forest, elastic net logistic regression, and XGBoost models using all variables and compared their performance to a baseline logistic regression model containing only demographics and retention history. RESULTS: We included people living with HIV with at least 2 HIV care encounters in the database, yielding 16,930 people living with HIV with 191,492 encounters. All models outperformed the baseline logistic regression model, with the most improvement from the XGBoost model (area under the receiver operating characteristic curve 0.776, 95% CI 0.768-0.784 vs 0.674, 95% CI 0.664-0.683; P<.001). Top predictors included the history of care lapses, being seen by an infectious disease provider (vs a primary care provider), site of care, Hispanic ethnicity, and previous HIV laboratory testing. The random forest model (area under the receiver operating characteristic curve 0.751, 95% CI 0.742-0.759) revealed age, insurance type, and chronic comorbidities (eg, hypertension), as important variables in predicting a care lapse. CONCLUSIONS: We used a real-world approach to leverage the full scope of data available in modern EHRs to predict HIV care lapses. Our findings reinforce previously known factors, such as the history of prior care lapses, while also showing the importance of laboratory testing, chronic comorbidities, sociodemographic characteristics, and clinic-specific factors for predicting care lapses for people living with HIV in Chicago. We provide a framework for others to use data from multiple different health care systems within a single city to examine lapses in care using EHR data, which will aid in jurisdictional efforts to improve retention in HIV care.

POWER Up-Improving pre-exposure prophylaxis (PrEP) uptake among Black cisgender women in the Southern United States: Protocol for a stepped-wedge cluster randomized trial (SW-CRT).

BACKGROUND: HIV disproportionately affects Black/African American cisgender women (hereafter women) in the United States. Despite its proven effectiveness, pre-exposure prophylaxis (PrEP) for HIV prevention remains vastly under-prescribed to women based on their need. Increasing PrEP uptake and persistence among women is crucial to reducing HIV transmission; however, there have been few studies designed specifically for women. This article describes the study protocol used to assess the feasibility, acceptability, and effectiveness of implementation strategies to improve PrEP uptake and persistence among Black women in the Midwest and South. METHODS: PrEP Optimization among Women to Enhance Retention and Uptake (POWER Up) is an evidence-based, woman-focused set of five implementation science strategies that addresses barriers of PrEP utilization at the provider, patient, and clinic levels. POWER Up includes 1) routine PrEP education for patients, 2) standardized provider training, 3) electronic medical record (EMR) optimization, 4) PrEP navigation, and 5) PrEP clinical champions. These strategies will be adapted to specific clinics for implementation, tested via a stepped-wedge trial, and, if effective, packaged for further dissemination. DISCUSSION: We will utilize a stepped-wedge cluster randomized trial (SW-CRT) to measure change in PrEP utilization across diverse geographic areas. Preparation for adapting and implementing the bundle of strategies is needed to determine how to tailor them to specific clinics. Implementation challenges will include adapting strategies with the available resources at each site, maintaining stakeholder involvement and staff buy-in, adjusting the study protocol and planned procedures as needed, and ensuring minimal crossover. Additionally, strengths and limitations of each strategy must be examined before, during, and after the adaptation and implementation processes. Finally, the implementation outcomes of the strategies must be evaluated to determine the real-world success of the strategies. This study is an important step toward addressing the inequity in PrEP service delivery and increasing PrEP utilization among Black women in the U.S.

No-show Prediction Model Performance Among People With HIV: External Validation Study.

BACKGROUND: Regular medical care is important for people living with HIV. A no-show predictive model among people with HIV could improve clinical care by allowing providers to proactively engage patients at high risk of missing appointments. Epic, a major provider of electronic medical record systems, created a model that predicts a patient's probability of being a no-show for an outpatient health care appointment; however, this model has not been externally validated in people with HIV. OBJECTIVE: We examined the performance of Epic's no-show model among people with HIV at an academic medical center and assessed whether the performance was impacted by the addition of demographic and HIV clinical information. METHODS: We obtained encounter data from all in-person appointments among people with HIV from January 21 to March 30, 2022, at the University of Chicago Medicine. We compared the predicted no-show probability at the time of the encounter to the actual outcome of these appointments. We also examined the performance of the Epic model among people with HIV for only HIV care appointments in the infectious diseases department. We further compared the no-show model among people with HIV for HIV care appointments to an alternate random forest model we created using a subset of seven readily accessible features used in the Epic model and four additional features related to HIV clinical care or demographics. RESULTS: We identified 674 people with HIV who contributed 1406 total scheduled in-person appointments during the study period. Of those, we identified 331 people with HIV who contributed 440 HIV care appointments. The performance of the Epic model among people with HIV for all appointments in any outpatient clinic had an area under the receiver operating characteristic curve (AUC) of 0.65 (95% CI 0.63-0.66) and for only HIV care appointments had an AUC of 0.63 (95% CI 0.59-0.67). The alternate model we created for people with HIV attending HIV care appointments had an AUC of 0.78 (95% CI 0.75-0.82), a significant improvement over the Epic model restricted to HIV care appointments (P<.001). Features identified as important in the alternate model included lead time, appointment length, HIV viral load >200 copies per mL, lower CD4 T cell counts (both 50 to <200 cells/mm3 and 200 to <350 cells/mm3), and female sex. CONCLUSIONS: For both models among people with HIV, performance was significantly lower than reported by Epic. The improvement in the performance of the alternate model over the proprietary Epic model demonstrates that, among people with HIV, the inclusion of demographic information may enhance the prediction of appointment attendance. The alternate model further reveals that the prediction of appointment attendance in people with HIV can be improved by using HIV clinical information such as CD4 count and HIV viral load test results as features in the model.

Clostridioides difficile colonization and the frequency of subsequent treatment for C. difficile infection in critically ill patients.

OBJECTIVE: To determine risk factors for Clostridioides difficile colonization and C. difficile infection (CDI) among patients admitted to the intensive care unit (ICU). DESIGN: Retrospective observational cohort study. SETTING: Tertiary-care facility. PATIENTS: All adult patients admitted to an ICU from July 1, 2015, to November 6, 2019, who were tested for C. difficile colonization. Patients with CDI were excluded. METHODS: Information was collected on patient demographics, comorbidities, laboratory results, and prescriptions. We defined C. difficile colonization as a positive nucleic acid amplification test for C. difficile up to 48 hours before or 24 hours after intensive care unit (ICU) admission without evidence of active infection. We defined active infection as the receipt of an antibiotic whose only indication is the treatment of CDI. The primary outcome measure was the development of CDI up to 30 days after ICU admission. Logistic regression was used to model associations between clinical variables and the development of CDI. RESULTS: The overall C. difficile colonization rate was 4% and the overall CDI rate was 2%. Risk factors for the development of CDI included C. difficile colonization (aOR, 13.3; 95% CI, 8.3-21.3; P < .0001), increased ICU length of stay (aOR, 1.04; 95% CI, 1.03-1.05; P < .0001), and a history of inflammatory bowel disease (aOR, 3.8; 95% CI, 1.3-11.1; P = .02). Receipt of any antibiotic during the ICU stay was associated with a borderline increased odds of CDI (aOR, 1.9; 95% CI, 1.0-3.4; P = .05). CONCLUSION: C. difficile colonization is associated with the development of CDI among ICU patients.

Measuring Retention in HIV Care in the First Year of the COVID-19 Pandemic: The Impact of Telehealth.

It is unknown how the COVID-19 pandemic impacted traditional measures of retention in HIV care. We calculated six different retention measures at an urban HIV care clinic for two time periods: pre-pandemic, and during the first year of the COVID-19 pandemic, with and without inclusion of telehealth appointments. Spearman rank correlation was used to assess correlation between different measures of retention. For both the pre-pandemic and pandemic time periods, there was strong correlation among measures of missed visits (range 0.857-0.957). More patients were considered retained in care during the pandemic when telehealth appointments were included in the analysis.

Odds of Hospitalization for COVID-19 After 3 vs 2 Doses of mRNA COVID-19 Vaccine by Time Since Booster Dose.

This study assesses the association between COVID-19 mRNA booster immunization compared with vaccination with the primary mRNA vaccination series alone and odds of hospitalization for COVID-19.

HIV testing in a high prevalence urban area in the US: Identifying missed opportunities two ways.

BACKGROUND: Routine opt-out HIV testing in healthcare settings is often not implemented to its fullest extent. We assessed factors contributing to missed HIV testing opportunities at an academic medical center in Chicago, Illinois, with a routine HIV screening program. METHODS: Retrospective analysis of HIV testing in clinical encounters was performed using multivariate regession models. Missed opportunities were defined as 1) an encounter during which an HIV test was not conducted on a patient later diagnosed with HIV, or 2) an encounter in which a bacterial STI test was performed without HIV testing. RESULTS: Of 122 people newly diagnosed with HIV from 2011-2018, 98 patients had 1215 prior encounters, of which 82.8% were missed opportunities. Female gender, persons not known to be men who have sex with men, and encounter location other than inpatient had higher odds of a missed opportunity. Nearly half (48.4%) of 104,678 bacterial STI testing encounters were missed opportunities. Female gender, older age, lack of syphilis testing, and location outside the emergency department had higher odds of a missed opportunity. CONCLUSIONS: We found a high number of missed HIV testing opportunities, which could be reduced by strengthening routine screening and increasing targeted testing concurrent with STI screening.

Decreased Risk of Coronavirus Disease 2019-Related Hospitalization Associated With the Omicron Variant of Severe Acute Respiratory Syndrome Coronavirus 2.

Among 134 223 patients with coronavirus disease 2019 (COVID-19), we assessed how risk of hospitalization changed at different intervals in the pandemic, controlling for prior COVID-19 immunity. In multivariable analysis, outpatients with COVID-19 during the Omicron-predominant time period had significantly lower odds of hospitalization compared to pre-Delta (adjusted odds ratio, 0.26 [95% confidence interval, .22-.32]).

Comparison of algorithms for identifying people with HIV from electronic medical records in a large, multi-site database.

Objective: As electronic medical record (EMR) data are increasingly used in HIV clinical and epidemiologic research, accurately identifying people with HIV (PWH) from EMR data is paramount. We sought to evaluate EMR data types and compare EMR algorithms for identifying PWH in a multicenter EMR database. Materials and Methods: We collected EMR data from 7 healthcare systems in the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) including diagnosis codes, anti-retroviral therapy (ART), and laboratory test results. Results: In total, 13 935 patients had a positive laboratory test for HIV; 33 412 patients had a diagnosis code for HIV; and 17 725 patients were on ART. Only 8576 patients had evidence of HIV-positive status for all 3 data types (laboratory results, diagnosis code, and ART). A previously validated combination algorithm identified 22 411 patients as PWH. Conclusion: EMR algorithms that combine laboratory results, administrative data, and ART can be applied to multicenter EMR data to identify PWH.

Provider Perspectives on Factors Affecting the PrEP Care Continuum Among Black Cisgender Women in the Midwest United States: Applying the Consolidated Framework for Implementation Research.

BACKGROUND: To End the HIV Epidemic and reduce the number of incident HIV infections in the United States by 90%, pre-exposure prophylaxis (PrEP) uptake and persistence among cisgender women, particularly racial and ethnic minority women, must be increased. Medical providers play a pivotal role across the PrEP care continuum. METHODS: In this qualitative study, guided by the Consolidated Framework for Implementation Research, we explored health care provider perspectives on facilitators and barriers to PrEP implementation strategies for Black cisgender women in the Midwest United States. Data were analyzed using a deductive thematic content analysis approach. RESULTS: A total of 10 medical providers completed individual qualitative interviews. Using the Consolidated Framework for Implementation Research framework, we identified intervention characteristics (cost, dosing, and adherence), individual patient and provider level factors (self-efficacy, knowledge, and attitudes), and systematic barriers (inner setting and outer setting) that ultimately lead to PrEP inequalities. Implementation strategies to improve the PrEP care continuum identified include provider training, electronic medical record optimization, routine patient education, and PrEP navigation. CONCLUSION: This study provides (1) medical provider insight into implementation factors that can be modified to improve the PrEP care continuum for Black cisgender women and (2) an implementation research logic model to guide future studies.

"Even if I'm undetectable, I just feel like I would die": a qualitative study to understand the psychological and socioeconomic impacts of the COVID-19 pandemic on women living with HIV (WLWH) in Chicago, IL.

BACKGROUND: The COVID-19 pandemic has affected the health and well-being of people worldwide, yet few studies have qualitatively examined its cumulative effects on ciswomen living with HIV (WLWH). We aimed to explore how the pandemic has impacted WLWH, including challenges related to HIV care, employment, finances, and childcare. We also investigated how HIV status and different psychosocial stressors affected their mental health. METHODS: We performed 25 semi-structured qualitative interviews with WLWH regarding the ways in which COVID-19 impacted their social determinants of health and physical well-being during the pandemic. 19 WLWH who received care at the University of Chicago Medicine (UCM) and 6 women who received care at Howard Brown Health, a federally qualified health center (FQHC) in Chicago, were interviewed remotely from June 2020 to April 2021. All interviews were audio recorded and transcribed. Interviews were thematically analyzed for commonalities regarding HIV-specific and general experiences of WLWH during the pandemic. RESULTS: The majority of participants reported COVID-19 impacted their HIV care, such as appointment cancellations and difficulties adhering to antiretroviral therapy. In addition to HIV care obstacles, almost all participants described perceived heightened vulnerability to or fear of COVID-19. The pandemic also affected the socioeconomic well-being of participants, with reported financial strains and employment disruptions. Some mothers took on additional childcare responsibilities, such as homeschooling. Increased mental health concerns and negative psychological effects from the social isolation associated with the pandemic were also experienced by most participants. CONCLUSIONS: We gained invaluable insight into how WLWH were challenged by and adapted to the COVID-19 pandemic, including its destabilizing effects on their HIV care and mental health. Women described how they undertook additional childcare responsibilities during the pandemic and how their HIV status compounded their concerns (e.g., perceived heightened vulnerability to COVID-19). Strategies to better support WLWH in maintaining their overall health throughout the pandemic include childcare assistance, access to affordable mental health services, support groups, and education from HIV care providers. These findings have significant implications for examining future health crises through the perspective of potential gender inequalities.

Multicenter Development and Validation of a Model for Predicting Retention in Care Among People with HIV.

Predictive analytics can be used to identify people with HIV currently retained in care who are at risk for future disengagement from care, allowing for prioritization of retention interventions. We utilized machine learning methods to develop predictive models of retention in care, defined as no more than a 12 month gap between HIV care appointments in the Center for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort. Data were split longitudinally into derivation and validation cohorts. We created logistic regression (LR), random forest (RF), and gradient boosted machine (XGB) models within a discrete-time survival analysis framework and compared their performance to a baseline model that included only demographics, viral suppression, and retention history. 21,267 Patients with 507,687 visits from 2007 to 2018 were included. The LR model outperformed the baseline model (AUC 0.68 [0.67-0.70] vs. 0.60 [0.59-0.62], P < 0.001). RF and XGB models had similar performance to the LR model. Top features in the LR model included retention history, age, and viral suppression.